Policy Implementation in Higher Education: The Dynamics of a Fall Break
Bibliographic record
Abstract
A case study using mixed methods that critically appraises the implementation of a mental health policy in higher education in the absence of evidence to inform the policy using an exemplar case from one mid-sized post-secondary institution was the motivation for this research. Explanation building was used to iteratively analyse data on rival explanations of the implementation of the fall break policy. Analyses from the surveys revealed that overall, only 36.9 per cent of students perceived an increase in workload before the break and only 29.6 per cent of students perceived an increase in workload after the break. However, the focus groups and professor interviews revealed that the timing of the fall break had an impact on how students and professors experienced the break and their perceptions on its impact on student mental health. If baseline data regarding the implementation of the fall break would have been collected prior to its implementation, we could have possibly avoided the implementation issues that arose. While this research provides an exemplar case of a fall break policy at one post-secondary institution, the policy learning is universal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".